1. Field of the Invention
The present invention relates to a machine learning device which learns a current command for a motor, a motor controller, and a machine learning method.
2. Description of the Related Art
Conventionally, a motor has been installed in a machine tool, a forming machine, an injection molding machine, an industrial machine, an industrial and service robot, or the like, and a motor controller which controls such a motor has been used. Further, as the motor (servo motor), for example, using a d-q three-phase coordinate transformation, a three-phase alternating-current permanent magnet synchronous motor (PMSM) has been widely used.
FIG. 8A and FIG. 8B are diagrams for illustrating characteristics of a typical motor, and FIG. 8A illustrates a relationship between a torque and a rotation speed of the motor, and FIG. 8B illustrates a relationship between a d-axis current (−Id) and a q-axis current (Iq) of the motor. As illustrated by characteristic curves CL1, CL2, and CL3 in FIG. 8A, for example, when the torque of the motor is set to magnitudes different from each other, tq1, tq2, and tq3, in a stable region Ra, the torques tq1, tq2, and tq3 as set are maintained and the rotation speed increases. Then, the relationship between −Id and Iq of the motor is fixed at points of reference signs P1, P2, and P3 in FIG. 8B.
Further, as illustrated by the characteristic curves CL1, CL2, and CL3 in FIG. 8A, when the rotation speed of the motor is further increased from the stable region Ra to a region Rb, the torque of the motor decreases from each of tq1. Tq2, and tq3. Then, the relationship between −Id and Iq of the motor changes in such a manner as illustrated by the characteristic curves CI1, CI2, and CI3 in FIG. 8B. Accordingly, the motor (servo motor) determines a current control parameter in accordance with characteristics thereof, which, however, takes a large number of steps, and due to changes of an inductance of the motor by the rotation speed and a current value or influences of magnetic saturation and the like, determining an optimal parameter is difficult.
Incidentally, hitherto, a synchronous motor controller which can achieve a fast torque response by obtaining a suitable operation command of the d-axis current, even when factors which determine a temperature, a torque, and a voltage of a synchronous motor vary, has been proposed (e.g., Japanese Laid-Open Patent Publication No. 2015-089236). Such synchronous motor controller includes a magnetic flux weakening control unit which outputs a d-axis current command value 1 relative to a speed and a voltage for achieving high speed rotation by a magnetic flux weakening control and a maximum torque control unit which outputs a d-axis current command value 2 relative to a q-axis current value for generating a maximum torque. In addition, it is configured that the d-axis current command values 1 and 2 are combined to be a final d-axis current command value of a d-axis current control unit, and a magnetic flux weakening correction unit which corrects at least one of an input signal and an output signal of the magnetic flux weakening control unit and a torque correction unit which corrects an input signal of the maximum torque control unit are provided.
As described above, hitherto, the synchronous motor controller which can achieve a fast torque response by obtaining a suitable current command for the motor, even when factors which determine a temperature, a torque, and a voltage of the motor vary, has been proposed.
However, determining a current control parameter in accordance with characteristics of the motor takes a large number of steps, and for example, due to changes of an inductance of the motor by a rotation speed and a current value of the motor or influences of magnetic saturation and the like, determining an optimal parameter is difficult.
In view of the problem of the conventional technique as described above, it is an object of the present invention to provide a machine learning device that can learn a current command for a motor which is optimal with respect to each motor, a motor controller, and a machine learning method.